Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming

The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon‐flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux‐ versus pool‐based carbon cycle variables and (2) the time points when temperature and CO2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data‐model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux‐related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool‐related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast‐turnover pools to various CO2 and warming treatments were observed sooner than slow‐turnover pools. Our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.

[1]  Shuang Ma,et al.  Data‐Constrained Projections of Methane Fluxes in a Northern Minnesota Peatland in Response to Elevated CO2 and Warming , 2017 .

[2]  Michael C Dietze,et al.  Prediction in ecology: a first-principles framework. , 2017, Ecological applications : a publication of the Ecological Society of America.

[3]  Shuang Ma,et al.  Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation , 2017 .

[4]  Atul K. Jain,et al.  Challenging terrestrial biosphere models with data from the long‐term multifactor Prairie Heating and CO2 Enrichment experiment , 2017, Global change biology.

[5]  D. Baldocchi,et al.  Evaluation of a hierarchy of models reveals importance of substrate limitation for predicting carbon dioxide and methane exchange in restored wetlands , 2017 .

[6]  G. Marion,et al.  A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming , 2001, Oecologia.

[7]  R. Norby,et al.  SPRUCE S1 Bog Fine-root Production and Standing Crop Assessed With Minirhizotrons in the Southern and Northern Ends of the S1 Bog , 2017 .

[8]  C. Schadt,et al.  Stability of peatland carbon to rising temperatures , 2016, Nature Communications.

[9]  G. Lin,et al.  Variation of parameters in a Flux-Based Ecosystem Model across 12 sites of terrestrial ecosystems in the conterminous USA , 2016 .

[10]  D. Weston,et al.  Intermediate-scale community-level flux of CO2 and CH4 in a Minnesota peatland: putting the SPRUCE project in a global context , 2016, Biogeochemistry.

[11]  Ke Zhang,et al.  Variation in stem mortality rates determines patterns of above‐ground biomass in Amazonian forests: implications for dynamic global vegetation models , 2016, Global change biology.

[12]  C. Schmullius,et al.  Large‐scale variation in boreal and temperate forest carbon turnover rate related to climate , 2016 .

[13]  J. Soussana,et al.  Elevated CO2 maintains grassland net carbon uptake under a future heat and drought extreme , 2016, Proceedings of the National Academy of Sciences.

[14]  Marc Macias-Fauria,et al.  Sensitivity of global terrestrial ecosystems to climate variability , 2016, Nature.

[15]  D. Tilman,et al.  Shifting grassland plant community structure drives positive interactive effects of warming and diversity on aboveground net primary productivity , 2016, Global change biology.

[16]  Yujie He,et al.  Toward more realistic projections of soil carbon dynamics by Earth system models , 2016 .

[17]  Anja Rammig,et al.  Model-data synthesis for the next generation of forest free-air CO2 enrichment (FACE) experiments. , 2016, The New phytologist.

[18]  Peijun Shi,et al.  Age‐dependent forest carbon sink: Estimation via inverse modeling , 2015 .

[19]  Dejun Li,et al.  Experimental warming altered rates of carbon processes, allocation, and carbon storage in a tallgrass prairie , 2015 .

[20]  Yiqi Luo,et al.  Plant community structure regulates responses of prairie soil respiration to decadal experimental warming , 2015, Global change biology.

[21]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[22]  Atul K. Jain,et al.  Using ecosystem experiments to improve vegetation models , 2015 .

[23]  Markus Reichstein,et al.  Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts , 2015, Global change biology.

[24]  D. Lawrence,et al.  Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study , 2015 .

[25]  Huimin Wang,et al.  Complementarity of flux- and biometric-based data to constrain parameters in a terrestrial carbon model , 2015 .

[26]  Courtney A. Schultz,et al.  Assessing Interactions Among Changing Climate, Management, and Disturbance in Forests: A Macrosystems Approach , 2015 .

[27]  Matthew J. Smith,et al.  Predictability of the terrestrial carbon cycle , 2015, Global change biology.

[28]  Yiqi Luo,et al.  Improvement of global litter turnover rate predictions using a Bayesian MCMC approach , 2014 .

[29]  X. Mo,et al.  Optimizing the photosynthetic parameter Vcmax by assimilating MODIS-fPAR and MODIS-NDVI with a process-based ecosystem model , 2014 .

[30]  A. Anthony Bloom,et al.  Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model-data fusion framework , 2014 .

[31]  M Luke McCormack,et al.  Variability in root production, phenology, and turnover rate among 12 temperate tree species. , 2014, Ecology.

[32]  F. Woodward,et al.  The relationship of leaf photosynthetic traits – Vcmax and Jmax – to leaf nitrogen, leaf phosphorus, and specific leaf area: a meta-analysis and modeling study , 2014, Ecology and evolution.

[33]  Atul K. Jain,et al.  Where does the carbon go? A model–data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites , 2014, The New phytologist.

[34]  Yiqi Luo,et al.  Faster Decomposition Under Increased Atmospheric CO2 Limits Soil Carbon Storage , 2014, Science.

[35]  Atul K. Jain,et al.  Comprehensive ecosystem model‐data synthesis using multiple data sets at two temperate forest free‐air CO2 enrichment experiments: Model performance at ambient CO2 concentration , 2014 .

[36]  Yiqi Luo,et al.  Evaluation and improvement of a global land model against soil carbon data using a Bayesian Markov chain Monte Carlo method , 2014 .

[37]  Roy Turkington,et al.  Response of grassland biomass production to simulated climate change and clipping along an elevation gradient , 2013, Oecologia.

[38]  M. Dietze.  Gaps in knowledge and data driving uncertainty in models of photosynthesis , 2013, Photosynthesis Research.

[39]  Xiaohui Feng,et al.  Scale dependence in the effects of leaf ecophysiological traits on photosynthesis: Bayesian parameterization of photosynthesis models. , 2013, The New phytologist.

[40]  S. Seneviratne,et al.  Climate extremes and the carbon cycle , 2013, Nature.

[41]  Joshua P. Schimel,et al.  Long-term warming restructures Arctic tundra without changing net soil carbon storage , 2013, Nature.

[42]  M. Rummukainen,et al.  GCM characteristics explain the majority of uncertainty in projected 21st century terrestrial ecosystem carbon balance , 2013 .

[43]  Hui Li,et al.  Responses of ecosystem carbon cycle to experimental warming: a meta-analysis. , 2013, Ecology.

[44]  Eric A Davidson,et al.  Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle. , 2013, Ecological applications : a publication of the Ecological Society of America.

[45]  Philippe Ciais,et al.  A framework for benchmarking land models , 2012 .

[46]  Benjamin Smith,et al.  Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections , 2012 .

[47]  Drew W. Purves,et al.  The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties , 2012 .

[48]  E. Davidson,et al.  Using model‐data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling , 2012 .

[49]  Charles T. Garten,et al.  SPRUCE S1 Bog Vegetation Allometric and Biomass Data: 2010-2011 , 2012 .

[50]  R. Kolka,et al.  SPRUCE Peat Physical and Chemical Characteristics from Experimental Plot Cores, 2012 , 2012 .

[51]  Yiqi Luo,et al.  Uncertainty analysis of forest carbon sink forecast with varying measurement errors: a data assimilation approach , 2011 .

[52]  Yiqi Luo,et al.  Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics. , 2011, Ecological applications : a publication of the Ecological Society of America.

[53]  Shenfeng Fei,et al.  Ecological forecasting and data assimilation in a data-rich era. , 2011, Ecological applications : a publication of the Ecological Society of America.

[54]  R. Kolka,et al.  Long-term monitoring sites and trends at the Marcell Experimental Forest. Chapter 2. , 2011 .

[55]  R. Kolka,et al.  at the Marcell Experimental Forest , 2011 .

[56]  D. Schimel,et al.  Concurrent and lagged impacts of an anomalously warm year on autotrophic and heterotrophic components of soil respiration: a deconvolution analysis. , 2010, The New phytologist.

[57]  S. Wofsy,et al.  Responses of terrestrial ecosystems and carbon budgets to current and future environmental variability , 2010, Proceedings of the National Academy of Sciences.

[58]  Li Zhang,et al.  Estimated carbon residence times in three forest ecosystems of eastern China: Applications of probabilistic inversion , 2010 .

[59]  E. Eccel.  What we can ask to hourly temperature recording. Part II: Hourly interpolation of temperatures for climatology and modelling. , 2010 .

[60]  Li Zhang,et al.  Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models. , 2009, Ecological applications : a publication of the Ecological Society of America.

[61]  I. C. Prentice,et al.  Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) , 2008 .

[62]  Bernhard Pfaff,et al.  VAR, SVAR and SVEC Models: Implementation Within R Package vars , 2008 .

[63]  W. Parton,et al.  Projected ecosystem impact of the Prairie Heating and CO2 Enrichment experiment. , 2007, The New phytologist.

[64]  Yiqi Luo,et al.  Source components and interannual variability of soil CO2 efflux under experimental warming and clipping in a grassland ecosystem , 2007 .

[65]  L. White,et al.  Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction , 2006 .

[66]  R. Ceulemans,et al.  Forest response to elevated CO2 is conserved across a broad range of productivity. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[67]  Ernst Linder,et al.  Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations , 2005 .

[68]  R. Norby,et al.  Evaluating ecosystem responses to rising atmospheric CO2 and global warming in a multi‐factor world , 2004 .

[69]  M. Wigmosta,et al.  Development of Hourly Meteorological Values From Daily Data and Significance to Hydrological Modeling at H.J. Andrews Experimental Forest , 2003 .

[70]  Philippe Ciais,et al.  How uncertainties in future climate change predictions translate into future terrestrial carbon fluxes , 2003 .

[71]  P Duce,et al.  An improved model for determining degree-day values from daily temperature data , 2001, International journal of biometeorology.

[72]  Yiqi Luo,et al.  Acclimatization of soil respiration to warming in a tall grass prairie , 2001, Nature.

[73]  S. Carpenter,et al.  Ecological forecasts: an emerging imperative. , 2001, Science.

[74]  F. Woodward,et al.  Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models , 2001 .

[75]  James F. Reynolds,et al.  VALIDITY OF EXTRAPOLATING FIELD CO2 EXPERIMENTS TO PREDICT CARBON SEQUESTRATION IN NATURAL ECOSYSTEMS , 1999 .